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Buyer persona x buyer journey matrix
I get a complete buyer-persona by buyer-journey matrix where every persona and stage combination has exactly one populated cell, so no segment or stage falls through a gap.
You receive: A JSON object { personas: [...], stages: [...], cells: [{ persona, stage, content }] } describing the coverage matrix.
Part of Build a Sales Engine
Example
A sample of what this play produces. Your result is generated for your inputs.
Personas
- Solo founder
- Small product team lead
- Ops or engineering lead adopting AI agents
Stages
- Discover
- Evaluate
- First run
- Compound
Cells
| Persona | Stage | Content |
|---|---|---|
| Solo founder | Discover | Finds STUD through content-led SEO and launch communities while searching for how to get marketing, finance, and legal work done without hiring. The hook is the living-system frame: a workspace that knows the business, does the work, and learns from every result, not another blank prompt box. |
| Solo founder | Evaluate | Compares STUD against the chat assistant already open in a tab. The persuading difference: 265 plays across 14 playbooks replace prompt craft, the intake fills itself from workspace facts, and results stay current instead of dying in a chat log. A 500-credit pack is $20, about one play, priced under one freelancer hour, and purchased credits never expire. |
| Solo founder | First run | States a handful of company facts once, then runs a first play from the catalog (a positioning one-pager or a pricing table). The deliverable lands structured, passes its play's acceptance check before it counts, and the founder sees a fact stated once flow into the next intake. |
| Solo founder | Compound | Spends about $25 a month on packs, roughly 1.25 plays at the typical 500-credit price (illustrative). Each result feeds later runs, so month-three deliverables read like they came from someone who knows the venture. The retention driver is accumulated workspace knowledge: leaving means starting from zero somewhere else. |
| Small product team lead | Discover | Hears about STUD through developer documentation and a teammate's shared deliverable. The draw is coverage: one system that handles marketing, finance, product, and engineering work while the team stays small. |
| Small product team lead | Evaluate | Evaluates whether outputs are dependable enough to circulate to the team. The mechanism that closes the gap: every deliverable passes its play's acceptance check before it lands, and plays cost 250 to 1,000 credits ($10 to $40), most at 500 credits ($20), so a first play costs less than one planning meeting. |
| Small product team lead | First run | Loads the team's shared facts into the workspace and commissions a play the whole team consumes, such as a competitor teardown or a launch checklist. Structured output means it drops straight into the team's docs without reformatting. |
| Small product team lead | Compound | Buys larger packs more often (a 2,500-credit pack is $100 with 250 bonus credits) as more of the team's recurring work moves onto plays. The workspace becomes the team's current source of truth: state a fact once and it reaches everything the team runs. |
| Ops or engineering lead adopting AI agents | Discover | Arrives via developer documentation while surveying agent tooling. STUD reads differently from agent frameworks: ready-made plays with defined intakes and acceptance checks, not another SDK to wire up. |
| Ops or engineering lead adopting AI agents | Evaluate | Stress-tests the acceptance-check mechanism: what exactly is enforced before a deliverable counts. The catalog's machine-checked plays answer that concretely, and the credit peg ($0.04 per credit) makes per-run cost predictable enough to budget. |
| Ops or engineering lead adopting AI agents | First run | Runs a structured play whose output slots into an existing process, such as a compliance register or a tech-debt sequence. The structured, machine-checked deliverable proves agents can produce work the org can consume without manual cleanup. |
| Ops or engineering lead adopting AI agents | Compound | Standardizes recurring ops work on plays and watches results feed each other: last month's register updates this month's plan. The lead becomes the internal champion who moves the org from ad-hoc prompting to a system that learns. |
Opens soon
Cost50 credits
ProtectionSpent when the run starts · returned only if STUD is at fault
STUD opens runs for everyone soon, on every play at once.
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